Translator Disclaimer
August 2019 Assessing the Causal Effect of Binary Interventions from Observational Panel Data with Few Treated Units
Pantelis Samartsidis, Shaun R. Seaman, Anne M. Presanis, Matthew Hickman, Daniela De Angelis
Statist. Sci. 34(3): 486-503 (August 2019). DOI: 10.1214/19-STS713

Abstract

Researchers are often challenged with assessing the impact of an intervention on an outcome of interest in situations where the intervention is nonrandomised, the intervention is only applied to one or few units, the intervention is binary, and outcome measurements are available at multiple time points. In this paper, we review existing methods for causal inference in these situations. We detail the assumptions underlying each method, emphasize connections between the different approaches and provide guidelines regarding their practical implementation. Several open problems are identified thus highlighting the need for future research.

Citation

Download Citation

Pantelis Samartsidis. Shaun R. Seaman. Anne M. Presanis. Matthew Hickman. Daniela De Angelis. "Assessing the Causal Effect of Binary Interventions from Observational Panel Data with Few Treated Units." Statist. Sci. 34 (3) 486 - 503, August 2019. https://doi.org/10.1214/19-STS713

Information

Published: August 2019
First available in Project Euclid: 11 October 2019

zbMATH: 07162134
MathSciNet: MR4017525
Digital Object Identifier: 10.1214/19-STS713

Rights: Copyright © 2019 Institute of Mathematical Statistics

JOURNAL ARTICLE
18 PAGES


SHARE
Vol.34 • No. 3 • August 2019
Back to Top